
import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		)




class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def __init__(   self						:str	,  a						:Optional[Any]	,  a						:Union[str, Any]=1_3	,  a						:Optional[int]=7	,  a						:Any=True	,  a						:Tuple=True	,  a						:str=True	,  a						:List[str]=True	,  a						:Union[str, Any]=9_9	,  a						:int=3_2	,  a						:int=5	,  a						:List[Any]=4	,  a						:int=3_7	,  a						:Optional[Any]="gelu"	,  a						:Union[str, Any]=0.1	,  a						:List[str]=0.1	,  a						:int=5_1_2	,  a						:Optional[Any]=1_6	,  a						:List[str]=2	,  a						:Union[str, Any]=0.02	,  a						:Tuple=4	,  )						->					str:
								__UpperCamelCase      :  Any       =   parent
								__UpperCamelCase      :  Any       =   batch_size
								__UpperCamelCase      :  Union[str, Any]       =   seq_length
								__UpperCamelCase      :  Dict       =   is_training
								__UpperCamelCase      :  Tuple       =   use_attention_mask
								__UpperCamelCase      :  Tuple       =   use_token_type_ids
								__UpperCamelCase      :  int       =   use_labels
								__UpperCamelCase      :  Optional[Any]       =   vocab_size
								__UpperCamelCase      :  Dict       =   hidden_size
								__UpperCamelCase      :  Optional[Any]       =   num_hidden_layers
								__UpperCamelCase      :  Dict       =   num_attention_heads
								__UpperCamelCase      :  List[Any]       =   intermediate_size
								__UpperCamelCase      :  Optional[int]       =   hidden_act
								__UpperCamelCase      :  int       =   hidden_dropout_prob
								__UpperCamelCase      :  List[Any]       =   attention_probs_dropout_prob
								__UpperCamelCase      :  Optional[int]       =   max_position_embeddings
								__UpperCamelCase      :  Any       =   type_vocab_size
								__UpperCamelCase      :  Optional[Any]       =   type_sequence_label_size
								__UpperCamelCase      :  List[str]       =   initializer_range
								__UpperCamelCase      :  Dict       =   num_choices







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Tuple:
								__UpperCamelCase      :  Union[str, Any]       =   ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size		)

								__UpperCamelCase      :  str       =   None
								if self.use_attention_mask:
															__UpperCamelCase      :  List[str]       =   random_attention_mask([self.batch_size, self.seq_length]		)

								__UpperCamelCase      :  Dict       =   None
								if self.use_token_type_ids:
															__UpperCamelCase      :  str       =   ids_tensor([self.batch_size, self.seq_length]	,  self.type_vocab_size		)

								__UpperCamelCase      :  List[str]       =   RobertaPreLayerNormConfig(
								    vocab_size=self.vocab_size	,  hidden_size=self.hidden_size	,  num_hidden_layers=self.num_hidden_layers	,  num_attention_heads=self.num_attention_heads	,  intermediate_size=self.intermediate_size	,  hidden_act=self.hidden_act	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  max_position_embeddings=self.max_position_embeddings	,  type_vocab_size=self.type_vocab_size	,  is_decoder=a	,  initializer_range=self.initializer_range	,  )

								return config, input_ids, token_type_ids, attention_mask







	def   _lowerCamelCase							(   self						:Any		)						->					Union[str, Any]:
								__UpperCamelCase      :  Dict       =   self.prepare_config_and_inputs()
								__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  str       =   config_and_inputs
								__UpperCamelCase      :  List[Any]       =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict







	def   _lowerCamelCase							(   self						:List[str]		)						->					int:
								__UpperCamelCase      :  List[str]       =   self.prepare_config_and_inputs()
								__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  List[Any]       =   config_and_inputs

								__UpperCamelCase      :  Tuple       =   True
								__UpperCamelCase      :  Dict       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
								__UpperCamelCase      :  Dict       =   ids_tensor([self.batch_size, self.seq_length]	,  vocab_size=2		)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)




@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	True

	_A								=	(
	    (
	        FlaxRobertaPreLayerNormModel,
	        FlaxRobertaPreLayerNormForCausalLM,
	        FlaxRobertaPreLayerNormForMaskedLM,
	        FlaxRobertaPreLayerNormForSequenceClassification,
	        FlaxRobertaPreLayerNormForTokenClassification,
	        FlaxRobertaPreLayerNormForMultipleChoice,
	        FlaxRobertaPreLayerNormForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)







	def   _lowerCamelCase							(   self						:List[str]		)						->					List[str]:
								__UpperCamelCase      :  List[str]       =   FlaxRobertaPreLayerNormModelTester(self		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Optional[int]:
								for model_class_name in self.all_model_classes:
															__UpperCamelCase      :  Dict       =   model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=a		)
															__UpperCamelCase      :  str       =   model(np.ones((1, 1)		)		)
															self.assertIsNotNone(a		)




@require_flax
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	@slow
	def   _lowerCamelCase							(   self						:Tuple		)						->					str:
								__UpperCamelCase      :  List[str]       =   FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=a		)

								__UpperCamelCase      :  List[Any]       =   np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]	,  dtype=jnp.intaa		)
								__UpperCamelCase      :  Union[str, Any]       =   model(a		)[0]
								__UpperCamelCase      :  List[str]       =   [1, 1_1, 5_0_2_6_5]
								self.assertEqual(list(output.shape		)	,  a		)
								# compare the actual values for a slice.
								__UpperCamelCase      :  List[Any]       =   np.array(
								    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]	,  dtype=np.floataa		)
								self.assertTrue(np.allclose(output[:, :3, :3]	,  a	,  atol=1E-4		)		)







	@slow
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Optional[int]:
								__UpperCamelCase      :  Dict       =   FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=a		)

								__UpperCamelCase      :  List[str]       =   np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]	,  dtype=jnp.intaa		)
								__UpperCamelCase      :  Any       =   model(a		)[0]
								# compare the actual values for a slice.
								__UpperCamelCase      :  Dict       =   np.array(
								    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]	,  dtype=np.floataa		)
								self.assertTrue(np.allclose(output[:, :3, :3]	,  a	,  atol=1E-4		)		)

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)